IDEAS home Printed from https://ideas.repec.org/p/abo/neswpt/w0248.html
   My bibliography  Save this paper

Reconsideration of a simple approach to quantile regression for panel data

Author

Listed:
  • Galina Besstremyannaya

    (Centre for Economic and Financial Research at New Economic School)

  • Sergei Golovan

    (New Economic School)

Abstract

The note discusses a fallacy in the approach proposed by Ivan Canay (2011, The Econometrics Journal) for constructing a computationally simple two-step estimator in a quantile regression model with quantile-independent fixed effects. We formally prove that the estimator gives an incorrect inference for the constant term due to violation of the assumption about additive expansion of the first-step estimator, which requires the independence of its terms. Our simulations show that Canay's confidence intervals for the constant term are wrong. Finally, we focus on the fact that finding a sqrt(nT) consistent within estimator, as required by Canay's procedure, may be problematic. We provide an example of a model, for which we formally prove the non-existence of such an estimator.

Suggested Citation

  • Galina Besstremyannaya & Sergei Golovan, 2018. "Reconsideration of a simple approach to quantile regression for panel data," Working Papers w0248, New Economic School (NES).
    • Handle: RePEc:abo:neswpt:w0248
    as

    Download full text from publisher

    File URL: https://www.nes.ru/files/Preprints-resh/WP248.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Ivan A. Canay, 2011. "A simple approach to quantile regression for panel data," Econometrics Journal, Royal Economic Society, vol. 14(3), pages 368-386, October.
    2. Koenker, Roger, 2004. "Quantile regression for longitudinal data," Journal of Multivariate Analysis, Elsevier, vol. 91(1), pages 74-89, October.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ben-Salha Ousama & Zmami Mourad, 2020. "The impact of private capital flows on economic growth in the MENA region," Economics and Business Review, Sciendo, vol. 6(3), pages 45-67, August.
    2. Boikos, Spyridon & Panagiotidis, Theodore & Voucharas, Georgios, 2022. "Financial development, reforms and growth," Economic Modelling, Elsevier, vol. 108(C).
    3. Stelian STANCU & Eugenia GRECU & Mirela Ionela ACELEANU & Daniela Livia TRAŞCĂ & Claudiu Tiberiu ALBULESCU, 2021. "Does Firm Size Matters for Firm Growth? Evidence from the Romanian Health Sector," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(1), pages 17-31, December.
    4. Tilov, Ivan & Weber, Sylvain, 2023. "Heterogeneity in price elasticity of vehicle kilometers traveled: Evidence from micro-level panel data," Energy Economics, Elsevier, vol. 127(PA).
    5. Liang Chen & Yulong Huo, 2019. "A Simple Estimator for Quantile Panel Data Models Using Smoothed Quantile Regressions," Papers 1911.04729, arXiv.org.
    6. Yoshibumi Makabe & Yoshihiko Norimasa, 2022. "The Term Structure of Inflation at Risk: A Panel Quantile Regression Approach," Bank of Japan Working Paper Series 22-E-4, Bank of Japan.
    7. Claudiu Tiberiu Albulescu & Matei Tămășilă & Ilie Mihai Tăucean, 2021. "The Nonlinear Relationship Between Firm Size and Growth in the Automotive Industry," Journal of Industry, Competition and Trade, Springer, vol. 21(3), pages 445-463, September.
    8. Yoshihiko Norimasa & Kazuki Ueda & Tomohiro Watanabe, 2021. "Emerging Economies' Vulnerability to Changes in Capital Flows: The Role of Global and Local Factors," Bank of Japan Working Paper Series 21-E-5, Bank of Japan.
    9. David Powell, 2022. "Quantile regression with nonadditive fixed effects," Empirical Economics, Springer, vol. 63(5), pages 2675-2691, November.
    10. Galina Besstremyannaya & Sergei Golovan, 2023. "Measuring heterogeneity in hospital productivity: a quantile regression approach," Journal of Productivity Analysis, Springer, vol. 59(1), pages 15-43, February.
    11. Lang, Jan Hannes & Rusnák, Marek & Greiwe, Moritz, 2023. "Medium-term growth-at-risk in the euro area," Working Paper Series 2808, European Central Bank.
    12. Battagliola, Maria Laura & Sørensen, Helle & Tolver, Anders & Staicu, Ana-Maria, 2022. "A bias-adjusted estimator in quantile regression for clustered data," Econometrics and Statistics, Elsevier, vol. 23(C), pages 165-186.
    13. Besstremyannaya, Galina & Golovan, Sergei, 2021. "Measuring heterogeneity with fixed effect quantile regression: Long panels and short panels," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 64, pages 70-82.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Savranlar, Buket & Atay Polat, Melike & Aslan, Alper, 2023. "What are the mistakes we think are correct about the ‘Natural resource curse’ hypothesis? New insights from quantile regressions via method of moments for EU," Resources Policy, Elsevier, vol. 85(PA).
    2. Jorge E. Galán & María Rodríguez Moreno, 2020. "At-risk measures and financial stability," Financial Stability Review, Banco de España, issue Autumn.
    3. Ibrahim Mohamed Ali Ali & Imed Attiaoui & Rabeh Khalfaoui & Aviral Kumar Tiwari, 2022. "The Effect of Urbanization and Industrialization on Income Inequality: An Analysis Based on the Method of Moments Quantile Regression," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 161(1), pages 29-50, May.
    4. Łukasz Jarosław Kozar & Robert Matusiak & Marta Paduszyńska & Adam Sulich, 2022. "Green Jobs in the EU Renewable Energy Sector: Quantile Regression Approach," Energies, MDPI, vol. 15(18), pages 1-21, September.
    5. Aldo Gutiérrez Mendieta, 2016. "Determinantes de consumo eficiente de energía eléctrica en el sector residencial en México: un enfoque de regresión cuantílica," Graduate theses (Spanish) TESG 010, CIDE, División de Economía.
    6. Gnangnon, Sèna Kimm, 2023. "The Least developed countries' TRIPS Waiver and the Strength of Intellectual Property Protection," EconStor Preprints 271537, ZBW - Leibniz Information Centre for Economics.
    7. Yu, Linyue & Wilcox-Gök, Virginia, 2015. "The impact of maternal depression on children’s cognitive development: An analysis based on panel quantile regressions," Economics Letters, Elsevier, vol. 126(C), pages 107-109.
    8. Miao, Yang & Razzaq, Asif & Adebayo, Tomiwa Sunday & Awosusi, Abraham Ayobamiji, 2022. "Do renewable energy consumption and financial globalisation contribute to ecological sustainability in newly industrialized countries?," Renewable Energy, Elsevier, vol. 187(C), pages 688-697.
    9. Mateut, Simona & Chevapatrakul, Thanaset, 2018. "Customer financing, bargaining power and trade credit uptake," International Review of Financial Analysis, Elsevier, vol. 59(C), pages 147-162.
    10. Galina Besstremyannaya & Sergei Golovan, 2023. "Measuring heterogeneity in hospital productivity: a quantile regression approach," Journal of Productivity Analysis, Springer, vol. 59(1), pages 15-43, February.
    11. Kazi Musa & Kazi Sohag & Jamaliah Said & Farha Ghapar & Norli Ali, 2023. "Public Debt, Governance, and Growth in Developing Countries: An Application of Quantile via Moments," Mathematics, MDPI, vol. 11(3), pages 1-13, January.
    12. Matheus Koengkan & José Alberto Fuinhas & Anna Auza & Uğur Ursavaş, 2023. "The Impact of Energy Efficiency Regulations on Energy Poverty in Residential Dwellings in the Lisbon Metropolitan Area: An Empirical Investigation," Sustainability, MDPI, vol. 15(5), pages 1-29, February.
    13. Nordman, Christophe J. & Rakotomanana, Faly & Roubaud, François, 2016. "Informal versus Formal: A Panel Data Analysis of Earnings Gaps in Madagascar," World Development, Elsevier, vol. 86(C), pages 1-17.
    14. Tobias Adrian & Federico Grinberg & Nellie Liang & Sheheryar Malik & Jie Yu, 2022. "The Term Structure of Growth-at-Risk," American Economic Journal: Macroeconomics, American Economic Association, vol. 14(3), pages 283-323, July.
    15. Aysit Tansel & Elif Oznur Acar, 2016. "The Formal/Informal Employment Earnings Gap: Evidence from Turkey," Research on Economic Inequality, in: Inequality after the 20th Century: Papers from the Sixth ECINEQ Meeting, volume 24, pages 121-154, Emerald Group Publishing Limited.
    16. Alessia Matano & Paolo Naticchioni, 2017. "The Extent of Rent Sharing along the Wage Distribution," British Journal of Industrial Relations, London School of Economics, vol. 55(4), pages 751-777, December.
    17. Eric Bartelsman & Sabien Dobbelaere & Bettina Peters, 2013. "Allocation of Human Capital and Innovation at the Frontier: Firm-level Evidence on Germany and the Netherlands," Tinbergen Institute Discussion Papers 13-095/VII, Tinbergen Institute.
    18. Antonio F. Galvao & Thomas Parker & Zhijie Xiao, 2021. "Bootstrap inference for panel data quantile regression," Papers 2111.03626, arXiv.org.
    19. Su, Chi Wei & Yue, Peiwen & Hou, Xinmeng & Dördüncü, Hazar, 2023. "Sustainable development through digital innovation: A new era for natural resource extraction and trade," Resources Policy, Elsevier, vol. 85(PB).
    20. repec:dau:papers:123456789/10601 is not listed on IDEAS
    21. Frantisek Cech & Jozef Barunik, 2017. "Measurement of Common Risk Factors: A Panel Quantile Regression Model for Returns," Working Papers IES 2017/20, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, revised Sep 2017.

    More about this item

    Keywords

    quantile regression; panel data; fixed effects; inference;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:abo:neswpt:w0248. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Vladimir Ivanyukhin (email available below). General contact details of provider: https://edirc.repec.org/data/nerasru.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.